Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer Questions and Answers

Use Cloud Bigtable for data storage.

Use Cloud SQL for data storage.

Use Cloud Spanner for data storage.

Use Firestore for data storage.

Review the Compute Engine activity logs Select and review the Admin Event logs

Review the Compute Engine activity logs Select and review the System Event logs

Install the Cloud Logging Agent In Cloud Logging review the Compute Engine syslog logs

Install the Cloud Logging Agent In Cloud Logging, review the Compute Engine operation logs

Grant yourself the IAM role of Cloud Spanner Admin

Create a new VPC network with subnetworks in all desired regions

Configure your Cloud Spanner instance to be multi-regional

Enable the Cloud Spanner API

Manual Scaling with 3 instances.

Basic Scaling with min_instances set to 3.

Basic Scaling with max_instances set to 3.

Automatic Scaling with min_idle_instances set to 3.

Use the Google Cloud Pricing Calculator to determine the cost of every Google Cloud resource you expect to use. Use similar size instances for the web server, and use your current on-premises machines as a comparison for Cloud SQL.

Implement a similar architecture on Google Cloud, and run a reasonable load test on a smaller scale. Check the billing information, and calculate the estimated costs based on the real load your system usually handles.

Use the Google Cloud Pricing Calculator and select the Cloud Operations template to define your web application with as much detail as possible.

Create a Google spreadsheet with multiple Google Cloud resource combinations. On a separate sheet, import the current Google Cloud prices and use these prices for the calculations within formulas.

Use gcloud iam roles copy and specify the production project as the destination project.

Use gcloud iam roles copy and specify your organization as the destination organization.

In the Google Cloud Platform Console, use the ‘create role from role’ functionality.

In the Google Cloud Platform Console, use the ‘create role’ functionality and select all applicable permissions.

In the Google Cloud console, visualize the costs related to the projects in the Reports section.

In the Google Cloud console, visualize the costs related to the projects in the Cost breakdown section.

In the Google Cloud console, use the export functionality of the Cost table. Create a Looker Studiodashboard on top of the CSV export.

Configure Cloud Billing data export to BigOuery for the billing account. Create a Looker Studio dashboard on top of the BigOuery export.

Attach a public IP to the instances and allow incoming connections from the internet on port 22 for SSH.

Use a third party tool to provide remote access to the instances.

Use the gcloud compute ssh command with the --tunnel-through-iap flag. Allow ingress traffic from the IP range 35.235.240.0/20 on port 22.

Create a bastion host with public internet access. Create the SSH tunnel to the instance through the bastion host.

Arrange to switch to Flat-Rate pricing for this query, then move back to on-demand.

Use the command line to run a dry run query to estimate the number of bytes read. Then convert that bytes estimate to dollars using the Pricing Calculator.

Use the command line to run a dry run query to estimate the number of bytes returned. Then convert that bytes estimate to dollars using the Pricing Calculator.

Run a select count (*) to get an idea of how many records your query will look through. Then convert that number of rows to dollars using the Pricing Calculator.

Increase the size of the disk to 1 TB.

Increase the allocated CPU to the instance.

Migrate to use a Local SSD on the instance.

Migrate to use a Regional SSD on the instance.

In the Log Viewer, filter the logs on severity 'Error' and the name of the Service Account.

Create a sink to BigQuery to export all the logs. Create a Data Studio dashboard on the exported logs.

Create a custom log-based metric for the specific error to be used in an Alerting Policy.

Grant Project Owner access to the Service Account.

Use App Engine and configure Cloud Scheduler to trigger the application using Pub/Sub.

Use Cloud Functions and configure the bucket as a trigger resource.

Use Google Kubernetes Engine and configure a CronJob to trigger the application using Pub/Sub.

Use Dataflow as a batch job, and configure the bucket as a data source.

Enable Private Service Access on the Cloud Storage Bucket.

Add slorage.googleapis.com to the list of restricted services in a VPC Service Controls perimeter and add your project to the list to protected projects.

Enable Private Google Access on the subnet within the custom VPC.

Deploy a Cloud NAT instance and route the traffic to the dedicated IP address of the Cloud Storage bucket.

Upload the image to Cloud Storage and create a Kubernetes Service referencing the image.

Upload the image to Cloud Storage and create a Kubernetes Deployment referencing the image.

Upload the image to Container Registry and create a Kubernetes Service referencing the image.

Upload the image to Container Registry and create a Kubernetes Deployment referencing the image.

Build a lifecycle policy to delete Cloud Storage objects after 45 days.

Use signed URLs to allow suppliers limited time access to store their objects.

Set up an SFTP server for your application, and create a separate user for each supplier.

Build a Cloud function that triggers a timer of 45 days to delete objects that have expired.

Develop a script that loops through all Cloud Storage buckets and deletes any buckets that are older than 45 days.

Migrate from MySQL to Cloud SQL, from Kafka to Memorystore, and from Cloud SQL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud Spanner, from Kafka to Memorystore, and from Cloud SOL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud SOL, from Kafka to Pub/Sub, and from Cloud SOL for PostgreSQL to BigQuery.

Migrate from MySQL to Cloud Spanner, from Kafka to Pub/Sub. and from Cloud SQL for PostgreSQL to BigQuery

Grant each individual external consultant the role of BigQuery Editor

Grant each individual external consultant the role of BigQuery Viewer

Create a Google Group that contains the consultants and grant the group the role of BigQuery Editor

Create a Google Group that contains the consultants, and grant the group the role of BigQuery Viewer

Create two configurations using gcloud config configurations create [NAME]. Run gcloud config configurations activate [NAME] to switch between accounts when running the commands to start the Compute Engine instances.

Create two configurations using gcloud config configurations create [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud config list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Go to Data Catalog and search for employee_ssn in the search box.

Write a shell script that uses the bq command line tool to loop through all the projects in your organization.

Write a script that loops through all the projects in your organization and runs a query on INFORMATION_SCHEMA.COLUMNS view to find the employee_ssn column.

Write a Cloud Dataflow job that loops through all the projects in your organization and runs a query on INFORMATION_SCHEMA.COLUMNS view to find employee_ssn column.

In Google Cloud, configure the VPC as a host for Shared VPC.

In Google Cloud, configure the VPC for VPC Network Peering.

Create bastion hosts both in your on-premises environment and on Google Cloud. Configure both as proxy servers using their public IP addresses.

Set up Cloud VPN between the infrastructure on-premises and Google Cloud.

Change the default region property setting in the existing GCP project to asia-northeast1.

Change the region property setting in the existing App Engine application from us-central to asia-northeast1.

Create a second App Engine application in the existing GCP project and specify asia-northeast1 as the region to serve your application.

Create a new GCP project and create an App Engine application inside this new project. Specify asia-northeast1 as the region to serve your application.

Deploy a Dataflow job from the batch template "Datastore lo Cloud Storage" Schedule the batch job on the desired interval

In the Cloud Console, go to Cloud Storage Upload the relevant images to the appropriate bucket

Create a script that uses the gsutil command line interface to synchronize the on-premises storage with Cloud Storage Schedule the script as a cron job

Create a Pub/Sub topic, and enable a Cloud Storage trigger for the Pub/Sub topic. Create an application that sends all medical images to the Pub/Sub lope

Add the users to roles/browser role.

Add the users to roles/iam.roleViewer role.

Add the users to a group, and add this group to roles/browser role.

Add the users to a group, and add this group to roles/iam.roleViewer role.

Create a Cloud Function to create an instance template.

Create a snapshot schedule for the disk using the desired interval.

Create a cron job to create a new disk from the disk using gcloud.

Create a Cloud Task to create an image and export it to Cloud Storage.

Grant the basic role roles/viewer and the predefined role roles/compute.admin to the DevOps group.

Create an IAM policy and grant all compute. instanceAdmln." permissions to the policy Attach the policy to the DevOps group.

Create a custom role at the folder level and grant all compute. instanceAdmln. * permissions to the role Grant the custom role to the DevOps group.

Grant the basic role roles/editor to the DevOps group.

Create the instance with the default Compute Engine service account Grant the service account permissions on Cloud Storage.

Create the instance with the default Compute Engine service account Add metadata to the objects on Cloud Storage that matches the metadata on the new instance.

Create a new service account and assig n this service account to the new instance Grant the service account permissions on Cloud Storage.

Create a new service account and assign this service account to the new instance Add metadata to the objects on Cloud Storage that matches the metadata on the new instance.

Google Compute Engine

Google App Engine

Cloud Functions

Google Kubernetes Engine

Update your web application to use the protocol HTTP/2 instead of HTTP/1.1

Set the concurrency number to 1 for your Cloud Run service.

Set the maximum number of instances for your Cloud Run service to 100.

Set the minimum number of instances for your Cloud Run service to 3.

1. Verify that you ace assigned the Billing Administrator IAM role tor your organization's Google Cloud Project for the Marketing department

2. Link the new project to a Marketing Billing Account

1. Verify that you are assigned the Billing Administrator IAM role for your organization's Google Cloud account

2. Create a new Google Cloud Project for the Marketing department

3. Set the default key-value project labels to department marketing for all services in this project

1. Verify that you are assigned the Organization Administrator IAM role for your organization's Google Cloud account

2. Create a new Google Cloud Project for the Marketing department 3. Link the new project to a Marketing Billing Account.

1. Verity that you are assigned the Organization Administrator IAM role for your organization's Google Cloud account

2. Create a new Google Cloud Project for the Marketing department

3. Set the default key value project labels to department marketing for all services in this protect

.00.0.0/0

10.0.0.0/8

172.16.0.0/12

192.168.0.0/16

• Add a step for human approval to the CI/CD pipeline before the execution of the infrastructure

provisioning.

• Use the human approvals IAM account for the provisioning.

• Attach a single service account to the compute instances.

• Add minimal rights to the service account.

• Allow the service account to impersonate a Cloud Identity user with elevated permissions to create, update, or delete resources.

• Attach a single service account to the compute instances.

• Add all required Identity and Access Management (IAM) permissions to this service account to create, update, or delete resources

• Create multiple service accounts, one for each pipeline with the appropriate minimal Identity and

Access Management (IAM) permissions.

• Use a secret manager service to store the key files of the service accounts.

• Allow the CI/CD pipeline to request the appropriate secrets during the execution of the pipeline.

Enable Audit Logs for all APIs that are related to data storage.

Review the IAM permissions for any role that allows for data access.

Review the Identity-Aware Proxy settings for each resource.

Create a Data Loss Prevention job.

Create one CNAME record to point mydomain.com to the load balancer, and create two A records to point WWW and HOME lo mydomain.com respectively.

Create one CNAME record to point mydomain.com to the load balancer, and create two AAAA records to point WWW and HOME to mydomain.com respectively.

Create one A record to point mydomain.com to the load balancer, and create two CNAME records to point WWW and HOME to mydomain.com respectively.

Create one A record to point mydomain.com lo the load balancer, and create two NS records to point WWW and HOME to mydomain.com respectively.

Use gcloud container clusters upgrade. Deploy the new services.

Create a new Node Pool and specify machine type n2–highmem–16. Deploy the new pods.

Create a new cluster with n2–highmem–16 nodes. Redeploy the pods and delete the old cluster.

Create a new cluster with both n1–standard–2 and n2–highmem–16 nodes. Redeploy the pods and delete the old cluster.

Ask the auditor for their Google account, and give them the Viewer role on the project.

Ask the auditor for their Google account, and give them the Security Reviewer role on the project.

Create a temporary account for the auditor in Cloud Identity, and give that account the Viewer role on the project.

Create a temporary account for the auditor in Cloud Identity, and give that account the Security Reviewer role on the project.

Use the GCP Console to transfer the file instead of gsutil.

Enable parallel composite uploads using gsutil on the file transfer.

Decrease the TCP window size on the machine initiating the transfer.

Change the storage class of the bucket from Nearline to Multi-Regional.

Modify the existing subnet range to 172.16.20.0/24.

Create a new Secondary IP Range in the VPC and configure the VMs to use that range.

Create a new VPC network for the VMs. Enable VPC Peering between the VMs’ VPC network and the Dataproc cluster VPC network.

Create a new VPC network for the VMs with a subnet of 172.32.0.0/16. Enable VPC network Peering between the Dataproc VPC network and the VMs VPC network. Configure a custom Route exchange.

Assign the appropriate permissions, and then use Cloud Monitoring to review metrics

Use the export logs API to provide the Admin Activity Audit Logs in the format they want

Turn on Data Access Logs for the buckets they want to audit, and Then build a query in the log viewer that filters on Cloud Storage

Assign the appropriate permissions, and then create a Data Studio report on Admin Activity Audit Logs

Create an Instance template with the container image, and deploy a Managed Instance Group with Autoscaling.

Upload Docker images to Artifact Registry, and deploy the application on Google Kubernetes Engine using Standard mode.

Upload Docker images to the Cloud Storage, and deploy the application on Google Kubernetes Engine using Standard mode.

Upload Docker images to Artifact Registry, and deploy the application on Cloud Run.

Set up a low-priority (65534) rule that blocks all egress and a high-priority rule (1000) that allows only the appropriate ports.

Set up a high-priority (1000) rule that pairs both ingress and egress ports.

Set up a high-priority (1000) rule that blocks all egress and a low-priority (65534) rule that allows only the appropriate ports.

Set up a high-priority (1000) rule to allow the appropriate ports.

An internal HTTP(S) load balancer together with Identity-Aware Proxy to allow only HTTPS traffic.

An external HTTP(S) load balancer to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend. Install the HTTPS certificates on the instance.

An external HTTP(S) load balancer with a managed SSL certificate to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend.

An external network load balancer pointing to the backend instances to distribute the load evenly. The web servers will forward the request to the Cloud Storage as needed.

Set metadata to enable-oslogin=true for the instance. Grant the dev1 group the compute.osLogin role. Direct them to use the Cloud Shell to ssh to that instance.

Set metadata to enable-oslogin=true for the instance. Set the service account to no service account for that instance. Direct them to use the Cloud Shell to ssh to that instance.

Enable block project wide keys for the instance. Generate an SSH key for each user in the dev1 group. Distribute the keys to dev1 users and direct them to use their third-party tools to connect.

Enable block project wide keys for the instance. Generate an SSH key and associate the key with that instance. Distribute the key to dev1 users and direct them to use their third-party tools to connect.

Deploy a new version of your application in Google Kubernetes Engine instead of App Engine and then use GCP Console to split traffic.

Deploy a new version of your application in a Compute Engine instance instead of App Engine and then use GCP Console to split traffic.

Deploy a new version as a separate app in App Engine. Then configure App Engine using GCP Console to split traffic between the two apps.

Deploy a new version of your application in App Engine. Then go to App Engine settings in GCP Console and split traffic between the current version and newly deployed versions accordingly.

In the console, validate which SSH keys have been stored as project-wide keys.

Navigate to Identity-Aware Proxy and check the permissions for these resources.

Enable Audit Logs on the IAM & admin page for all resources, and validate the results.

Use the command gcloud projects get–iam–policy to view the current role assignments.

Coldline Storage

Nearline Storage

Regional Storage

Multi-Regional Storage

Expose the application by using an external TCP Network Load Balancer.

Expose the application by using a TCP Proxy Load Balancer.

Expose the application by using an SSL Proxy Load Balancer.

Expose the application by using an internal TCP Network Load Balancer.

Run gcloud builds submit --tag gcr.io/img-278322/acme_track_n_trace

Run gcloud builds submit --tag gcr.io/img-278322/acme_track_n_trace:v1

Run gcloud builds submit --tag gcr.io/prod-278986/acme_track_n_trace

Run gcloud builds submit --tag gcr.io/prod-278986/acme_track_n_trace:v1

Assign the finance team only the Billing Account User role on the billing account.

Assign the engineering team only the Billing Account User role on the billing account.

Assign the finance team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.

Assign the engineering team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.

1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend.2. Set the service's externalTrafficPolicy to Cluster.3. Configure the Compute Engine instance to use the address of the load balancer that has been created.

1. In GKE, create a Service of type NodePort that uses the application's Pods as backend.2. Create a Compute Engine instance called proxy with 2 network interfaces, one in each VPC.3. Use iptables on this instance to forward traffic from gce-network to the GKE nodes.4. Configure the Compute Engine instance to use the address of proxy in gce-network as endpoint.

1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend.2. Add an annotation to this service: cloud.google.com/load-balancer-type: Internal3. Peer the two VPCs together.4. Configure the Compute Engine instance to use the address of the load balancer that has been created.

1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend.2. Add a Cloud Armor Security Policy to the load balancer that whitelists the internal IPs of the MIG's instances.3. Configure the Compute Engine instance to use the address of the load balancer that has been created.

Split the users from business units to multiple projects.

Apply a user- or project-level custom query quota for BigQuery data warehouse.

Create separate copies of your BigQuery data warehouse for each business unit.

Split your BigQuery data warehouse into multiple data warehouses for each business unit.

Change your BigQuery query model from on-demand to flat rate. Apply the appropriate number of slots to each Project.

Deploy the web application on Google Kubernetes Engine standard edition with an internal ingress.

Deploy the web application on Cloud Run with Private Google Access configured

Deploy the web application to GKE Autopilot with Private Google Access configured

Deploy the web application on Cloud Run with Private Service Connect configured.

Create a signed URL with a four-hour expiration and share the URL with the company.

Set object access to ‘public’ and use object lifecycle management to remove the object after four hours.

Configure the storage bucket as a static website and furnish the object’s URL to the company. Delete the object from the storage bucket after four hours.

Create a new Cloud Storage bucket specifically for the external company to access. Copy the object to that bucket. Delete the bucket after four hours have passed.

Deploy the container on Cloud Run for Anthos, and set the minimum number of instances to zero

Deploy the container on Cloud Run (fully managed), and set the minimum number of instances to zero.

Deploy the container on App Engine flexible environment with autoscaling. and set the value min_instances to zero in the app yaml

Deploy the container on App Engine flexible environment with manual scaling, and set the value instances to zero in the app yaml

Use permissions in your role that use the ‘supported’ support level for role permissions. Set the role stage to ALPHA while testing the role permissions.

Use permissions in your role that use the ‘supported’ support level for role permissions. Set the role stage to BETA while testing the role permissions.

Use permissions in your role that use the ‘testing’ support level for role permissions. Set the role stage to ALPHA while testing the role permissions.

Use permissions in your role that use the ‘testing’ support level for role permissions. Set the role stage to BETA while testing the role permissions.

Search errors in Cloud Audit Logs to analyze the issue.

Configure Cloud Trace to analyze the issue.

View errors in Cloud Monitoring to analyze the issue.

Use the information schema views to analyze the underlying issue.

Use BigQuery Bl Engine to analyze the issue.

Add the user to roles/iam.roleAdmin role.

Add the user to roles/iam.securityAdmin role.

Add the user to roles/iam.serviceAccountUser role.

Add the user to roles/iam.serviceAccountAdmin role.

Deploy the application on Cloud Functions.

Deploy the application on Cloud Run.

Deploy the application on GKE Standard.

Deploy the application on GKE Autopilot.

Create a Kubernetes Service of type NodePort for your application, and a Kubernetes Ingress to expose this Service via a Cloud Load Balancer.

Create a Kubernetes Service of type ClusterIP for your application. Configure the public DNS name of your application using the IP of this Service.

Create a Kubernetes Service of type NodePort to expose the application on port 443 of each node of the Kubernetes cluster. Configure the public DNS name of your application with the IP of every node of the cluster to achieve load-balancing.

Create a HAProxy pod in the cluster to load-balance the traffic to all the pods of the application. Forward the public traffic to HAProxy with an iptable rule. Configure the DNS name of your application using the public IP of the node HAProxy is running on.

Use a Shielded VM.

Use a Preemptible VM.

Use a sole-tenant node.

Enable deletion protection on the instance.

Verify that you are the project billing administrator. Select the associated billing account and create a budget and alert for the appropriate project.

Verify that you are the project billing administrator. Select the associated billing account and create a budget and a custom alert.

Verify that you are the project administrator. Select the associated billing account and create a budget for the appropriate project.

Verify that you are project administrator. Select the associated billing account and create a budget and a custom alert.

In Cloud Identity, set up SSO with Google as an identity provider to access custom SAML apps.

In Cloud Identity, set up SSO with a third-party identity provider with Google as a service provider.

Obtain OAuth 2.0 credentials, configure the user consent screen, and set up OAuth 2.0 for Mobile & Desktop Apps.

Obtain OAuth 2.0 credentials, configure the user consent screen, and set up OAuth 2.0 for Web Server Applications.

Use Cloud Logging filters to create log-based metrics for firewall and instance actions. Monitor the changes and set up reasonable alerts.

Install Kibana on a compute Instance. Create a log sink to forward Cloud Audit Logs filtered for firewalls and

compute instances to Pub/Sub. Target the Pub/Sub topic to push messages to the Kibana instance. Analyze the logs on Kibana in real time.

Turn on Google Cloud firewall rules logging, and set up alerts for any insert, update, or delete events.

Create a log sink to forward Cloud Audit Logs filtered for firewalls and compute instances to Cloud Storage.

Use BigQuery to periodically analyze log events in the storage bucket.

Create an instance template that contains valid syntax which will be used by the instance group. Delete any persistent disks with the same name as instance names.

Create an instance template that contains valid syntax that will be used by the instance group. Verify that the instance name and persistent disk name values are not the same in the template.

Verify that the instance template being used by the instance group contains valid syntax. Delete any persistent disks with the same name as instance names. Set the disks.autoDelete property to true in the instance template.

Delete the current instance template and replace it with a new instance template. Verify that the instance name and persistent disk name values are not the same in the template. Set the disks.autoDelete property to true in the instance template.

gcloud deployment-manager deployments create --config

gcloud deployment-manager deployments update --config

gcloud deployment-manager resources create --config

gcloud deployment-manager resources update --config

Create an HTTP load balancer with a backend configuration that references an existing instance group. Set the health check to healthy (HTTP).

Create an HTTP load balancer with a backend configuration that references an existing instance group. Define a balancing mode and set the maximum RPS to 10.

Create a managed instance group. Set the Autohealing health check to healthy (HTTP).

Create a managed instance group. Verify that the autoscaling setting is on.

Enable API and then share charts from project A, B, and C.

Enable API and then give the metrics.reader role to projects A, B, and C.

Enable API and then use default dashboards to view all projects in sequence.

Enable API, create a workspace under project A, and then add project B and C.

Select Multi-Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Coldline Storage.

Select Multi-Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Nearline Storage.

Select Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Nearline Storage.

Select Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Coldline Storage.

Use your existing CI/CD pipeline Use the generated Docker images and deploy them to Cloud Run. Update the configurations and the required endpoints.

Use your existing continuous integration and delivery (CI/CD) pipeline. Use the generated Docker images and deploy them to Cloud Function. Use the same configuration as on-premises.

Use the existing codebase and deploy each service as a separate Cloud Function Update the configurations and the required endpoints.

Use your existing codebase and deploy each service as a separate Cloud Run Use the same configurations as on-premises.

Create a bash script that contains all requirement steps as gcloud commands

Develop templates for the environment using Cloud Deployment Manager

Use curl in a terminal to send a REST request to the relevant Google API for each individual resource.

Use the Cloud Console interface to provision and manage all related resources

Deploy the application on GKE, and add a HorizontalPodAutoscaler to the deployment.

Deploy the application on GKE, and add a VerticalPodAutoscaler to the deployment.

Create a GKE cluster with autoscaling enabled on the node pool. Set a minimum and maximum for the size of the node pool.

Create a separate node pool for each application, and deploy each application to its dedicated node pool.

Set up a budget alert on the project with an amount of 100 dollars, a threshold of 100%, and notification type of “email.”

Set up a budget alert on the billing account with an amount of 100 dollars, a threshold of 100%, and notification type of “email.”

Export the billing data to BigQuery. Create a Cloud Function that uses BigQuery to sum the egress network costs of the exported billing data for the Apache web server for the current month and sends an email if it is over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

Use the Stackdriver Logging Agent to export the Apache web server logs to Stackdriver Logging. Create a Cloud Function that uses BigQuery to parse the HTTP response log data in Stackdriver for the current month and sends an email if the size of all HTTP responses, multiplied by current GCP egress prices, totals over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

Use labels to group resources that share common IAM policies

Use folders to group resources that share common IAM policies

Set up a proper billing account structure to group IAM policies

Set up a proper project naming structure to group IAM policies

Create an alert policy to send a notification when the HTTP response latency exceeds the specified threshold.

Implement an App Engine service which invokes the Cloud Monitoring API and sends a notification in case of anomalies.

Use the Cloud Monitoring dashboard to observe latency and take the necessary actions when the response latency exceeds the specified threshold.

Export Cloud Monitoring metrics to BigQuery and use a Looker Studio dashboard to monitor your web applications latency.

Multi-Regional Storage

Regional Storage

Nearline Storage

Coldline Storage

Use a Deployment Manager script to automate creating storage buckets in an appropriate region

Use a local SSD to improve performance of the VM for the targeted workload

Use the gsutii command to create a storage bucket in the same region as the VM

Use a Persistent Disk SSD in the same zone as the VM to improve performance of the VM

Create a cron job that runs on a scheduled basis to review stackdriver monitoring metrics, and then resize the Spanner instance accordingly.

Create a Stackdriver alerting policy to send an alert to oncall SRE emails when Cloud Spanner CPU exceeds the threshold. SREs would scale resources up or down accordingly.

Create a Stackdriver alerting policy to send an alert to Google Cloud Support email when Cloud Spanner CPU exceeds your threshold. Google support would scale resources up or down accordingly.

Create a Stackdriver alerting policy to send an alert to webhook when Cloud Spanner CPU is over or under your threshold. Create a Cloud Function that listens to HTTP and resizes Spanner resources accordingly.

Use the gsutil to rewrite the storage class for the bucket Change the default storage class for the bucket

Use the gsutil to rewrite the storage class for the bucket Set up Object Lifecycle management on the bucket

Create a new bucket and change the default storage class for the bucket Set up Object Lifecycle management on lite bucket

Create a new bucket and change the default storage class for the bucket import the files from the previous bucket into the new bucket

Deploy the monitoring pod in a StatefulSet object.

Deploy the monitoring pod in a DaemonSet object.

Reference the monitoring pod in a Deployment object.

Reference the monitoring pod in a cluster initializer at the GKE cluster creation time.

Ask your ML team to add the “accelerator: gpu” annotation to their pod specification.

Recreate all the nodes of the GKE cluster to enable GPUs on all of them.

Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.

Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

Run gcloud projects list to get the project ID, and then run gcloud services list --project .

Run gcloud init to set the current project to my-project, and then run gcloud services list --available.

Run gcloud info to view the account value, and then run gcloud services list --account .

Run gcloud projects describe to verify the project value, and then run gcloud services list --available.

Create Java packages that utilize the Google Cloud Client Libraries for Java to configure Google Cloud products. Store and share the packages in a source code repository.

Create bash scripts that utilize the Google Cloud CLI to configure Google Cloud products. Store and share the bash scripts in a source code repository.

Create Terraform modules that utilize the Google Cloud Terraform Provider to configure Google Cloud products. Store and share the modules in a source code repository.

Use the Google Cloud APIs by using curl to configure Google Cloud products. Store and share the curl commands in a source code repository.